Identification of changes in gene expression
Lucia Ameis, Kathrin M\"ollenhoff

TL;DR
This paper introduces a parametric method using bootstrap confidence bands to accurately identify when gene expression significantly changes over time, improving upon traditional t-test approaches.
Contribution
The paper presents a novel parametric approach with bootstrap confidence bands for detecting change points in gene expression time-course data.
Findings
Method accurately detects change points in simulated data.
Application to mouse data reveals diet-related gene expression changes.
Bootstrap approach is flexible for various curve types.
Abstract
Evaluating the change in gene expression is a common goal in many research areas, such as in toxicological studies as well as in clinical trials. In practice, the analysis is often based on multiple t-tests evaluated at the observed time points. This severely limits the accuracy of determining the time points at which the gene changes in expression. Even if a parametric approach is chosen, the analysis is often restricted to identifying the onset of an effect. In this paper, we propose a parametric method to identify the time frame where the gene expression significantly changes. This is achieved by fitting a parametric model to the time-response data and constructing a confidence band for its first derivative. The confidence band is derived by a flexible two step bootstrap approach, which can be applied to a wide variety of possible curves. Our method focuses on the first derivative,…
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Taxonomy
TopicsGene expression and cancer classification
